I. Field of the Invention
The embodiments disclosed in this application generally relate to dynamic recognition technologies used for recognizing static handwritten and machine printed text.
II. Background of the Invention
Dynamic Handwriting Recognition provides real time interpretation of handwritten strokes and is used primarily within Tablet personal computer (PC) or personal digital assistant (PDA) environments. In the Tablet PC application, the dynamic recognizer receives real-time handwriting data provided directly by the writer using a stylus. Based on the movements of the stylus, a digital representation of handwritten strokes can be captured as words are written. Basically, the stylus and pad interface is used to capture the strokes and convert them into ordered sequences of coordinates. These strokes are stored as data that is frequently referred to as “digital ink”. Digital ink consists of geometric plots of the strokes, stroke sequence, pen pressure, pen angle, and the like. Of these features, the ones most important for recognition are the geometry of the strokes and the sequence in which they were written. The digital ink is passed to the Dynamic Handwriting Recognition software to identify the handwriting data (e.g., character, word segment, word, etc.). Because of the rich set of features that Dynamic Handwriting Recognition draws from, it has achieved very significant accuracy levels.
Unlike Dynamic Handwriting Recognition where user input is captured in real-time, Static Handwriting Recognition involves capturing data from images of scanned documents. The images store only handwriting or machine generated data in a static form. As such, Static Handwriting Recognition draws upon fewer possible features than Dynamic Handwriting Recognition and therefore has achieved lesser levels of accuracy.
For instance, many features (e.g., stroke direction, stroke sequence, pen pressure, etc.) used for Dynamic Recognition that are captured while the actual writing is taking place are not present in scanned static images of handwriting and machine generated text. Therefore, Dynamic Recognition technology is not directly applicable for Static Recognition tasks such as text (e.g., handwriting, machine generated text, etc.) conversion from scanned documents.